Memory-assisted reinforcement learning for diverse molecular de novo design

Memory-assisted reinforcement learning for diverse molecular de novo design

Abstract In de novo molecular design, recurrent neural networks (RNN) have been shown to be effective methods for sampling and generating novel chemical structures. Using a technique called reinforcement learning (RL), an RNN can be tuned to target a particular section of chemical space with optimiz...

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Bibliographic Details
Main Authors: Thomas Blaschke, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Format: Article
Language:English
Published: BMC 2020-11-01
Series:Journal of Cheminformatics
Subjects:
Deep learning applications
Reinforcement learning
De Novo design
Exploration strategy
Recurrent neural networks
Online Access:http://link.springer.com/article/10.1186/s13321-020-00473-0

Internet

http://link.springer.com/article/10.1186/s13321-020-00473-0

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